Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system to facilitate software development and operations for an enterprise, comprising: (a) a communication input port to receive information associated with a software continuous integration and/or deployment pipeline of the enterprise; (b) an intelligent software agent platform, coupled to the communication input port, including a computer processor and a memory storing instructions to cause the computer processor to: (i) listen for a trigger indication from the software continuous integration and/or deployment pipeline, (ii) responsive to the trigger indication, apply system configuration information and rule layer information to extract software log data, and (iii) apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report, wherein the pipeline health check analysis report includes an automatically generated prediction associated with future operation of the software continuous integration and/or deployment pipeline; and (c) a communication output port coupled to the intelligent software agent platform to facilitate transmission of the pipeline health check analysis report via a distributed communication network, wherein the pipeline health check analysis report includes: a prediction of a level of stability of the software continuous integration and/or deployment pipeline; a prediction of a future type of failure of the software continuous integration and/or deployment pipeline; a listing of a software object associated with an error condition a number of test jobs associated with an application program; a number of jobs triggered that were associated with the application program; and a percentage of software builds that have successively passed through the software continuous integration and/or deployment pipeline during a time period.
The system is designed to enhance software development and operations (DevOps) by monitoring and analyzing continuous integration and deployment (CI/CD) pipelines in enterprise environments. The system addresses challenges in maintaining pipeline stability, predicting failures, and optimizing software delivery processes. It includes a communication input port to receive pipeline data, an intelligent software agent platform, and a communication output port for transmitting analysis results. The intelligent software agent platform processes pipeline data using system configuration and rule-based logic to extract relevant log data. A machine learning model analyzes this data to generate a pipeline health check report, which predicts future pipeline stability, potential failure types, and identifies software objects with errors. The report also provides metrics such as the number of test jobs, triggered jobs, and the success rate of builds over a specified time period. This predictive analysis helps enterprises proactively address issues, reduce downtime, and improve CI/CD pipeline efficiency. The system transmits the report via a distributed network, enabling real-time decision-making and continuous improvement in software deployment processes.
2. The system of claim 1 , wherein the machine learning model is associated with a knowledge map of the software continuous integration and/or deployment pipeline that classifies errors in the software log data.
3. The system of claim 1 , wherein the software continuous integration and/or deployment pipeline includes at least one of: (i) code and build components, (ii) static code analysis, (iii) deployment, (iv) build completion, (iv) a test trigger, (v) a performance measurement component, and (vi) any other type of pipeline component.
4. The system of claim 1 , wherein the system configuration information includes at least one of: (i) a pipeline configuration, (ii) a user configuration, and (iii) a log properties configuration.
5. The system of claim 4 , wherein the system configuration information includes at least one of: (i) stakeholder email addresses, (ii) line of business identifiers, (iii) jobs to be monitored, (iv) a monitoring range, (v) a pipeline stability threshold, (vi) a test case failure threshold, (vii) quality control login and configuration details, (viii) an automatic trigger time, (ix) an error classification, (x) application and value stream mapping, and (xi) multi-environment configuration information.
6. The system of claim 1 , wherein the generation of the pipeline health check analysis report is performed by a view generator including at least one of: (i) a build level generator, (ii) a line of business level generator, and (iii) an enterprise level generator.
7. The system of claim 1 , wherein the pipeline health check analysis report is transmitted to an automation framework and includes at least one of a self-healing analysis and a recommended corrective action.
A system for monitoring and maintaining the health of data processing pipelines generates a pipeline health check analysis report. The report includes diagnostic information about the pipeline's performance, identifying issues such as bottlenecks, errors, or inefficiencies. The system transmits this report to an automation framework, which processes the data to determine corrective actions. The report may include a self-healing analysis, where the system automatically identifies and applies fixes to resolve detected issues without manual intervention. Additionally, the report may provide recommended corrective actions, suggesting steps that administrators or automated systems can take to improve pipeline performance. These recommendations may include configuration changes, resource allocation adjustments, or code modifications. The automation framework uses the report to trigger automated remediation workflows or alert operators, ensuring timely resolution of pipeline issues. This approach reduces downtime and improves the reliability of data processing pipelines by proactively addressing potential failures.
8. The system of claim 1 , wherein the pipeline health check analysis report comprises an email message transmitted to at least one of: (i) a subject matter expert, (ii) a software development engineer in test, (iii) a software manager, (iv) a quality control member, (v) a quality assurance member, or (vi) any other stakeholder.
9. The system of claim 1 , wherein the pipeline health check analysis report is used to automatically transmit a remote access Application Programming Interface (“API”) console output to the software continuous integration and/or deployment pipeline.
10. The system of claim 1 , wherein the pipeline health check analysis report includes information about multiple software continuous integration and/or deployment pipelines.
11. The system of claim 1 , wherein the pipeline health check analysis report includes at least one recommended action.
12. The system of claim 1 , wherein the intelligent software agent platform is further to integrate the pipeline health check analysis report into a dashboard display.
13. The system of claim 1 , wherein the machine learning model is associated with at least one of: (i) artificial intelligence, (ii) supervised learning, (iii) semi-supervised learning, (iv) weakly supervised learning, (v) unsupervised learning, (vi) reinforcement learning, (vii) feature learning, (viii) sparse dictionary learning, (ix) anomaly detection, (x) decision trees, (xi) association rules, (xii) an artificial neural network, (xiii) a support vector machine, (xiv) a Bayesian network, and (xv) a genetic algorithm.
14. A computerized method to facilitate software development and operations for an enterprise, comprising: listening, by an intelligent software agent platform, to information from a communication input port associated with a software continuous integration and/or deployment pipeline of the enterprise, wherein the intelligent software agent platform is listening for a trigger indication from the software continuous integration and/or deployment pipeline; responsive to the trigger indication, applying system configuration information and rule layer information to extract software log data; applying a machine learning model to the extracted software log data to generate a pipeline health check analysis report, wherein the pipeline health check analysis report includes an automatically generated prediction associated with future operation of the software continuous integration and/or deployment pipeline; and transmitting, via a communication output port coupled to the intelligent software agent platform, the pipeline health check analysis report via a distributed communication network; wherein the pipeline health check analysis report includes: a prediction of a level of stability of the software continuous integration and/or deployment pipeline; a prediction of a future type of failure of the software continuous integration and/or deployment pipeline; a listing of a software object associated with an error condition a number of test jobs associated with an application program; a number of jobs triggered that were associated with the application program; and a percentage of software builds that have successively passed through the software continuous integration and/or deployment pipeline during a time period.
15. The method of claim 14 , wherein the software continuous integration and/or deployment pipeline includes at least one of: (i) code and build components, (ii) static code analysis, (iii) deployment, (iv) build completion, (iv) a test trigger, (v) a performance measurement component, and (vi) any other type of pipeline component.
16. The method of claim 14 , wherein the system configuration information includes at least one of: (i) a pipeline configuration, (ii) a user configuration, and (iii) a log properties configuration.
17. The method of claim 14 , wherein the generation of the pipeline health check analysis report is performed by a view generator including at least one of: (i) a build level generator, (ii) a line of business level generator, and (iii) an enterprise level generator.
18. A non-transitory, computer-readable medium storing instructions, that, when executed by a processor, cause the processor to perform a method to facilitate software development and operations for an enterprise, the method comprising: listening, by an intelligent software agent platform, to information from a communication input port associated with a software continuous integration and/or deployment pipeline of the enterprise, wherein the intelligent software agent platform is listening for a trigger indication from the software continuous integration and/or deployment pipeline; responsive to the trigger indication, applying system configuration information and rule layer information to extract software log data; applying a machine learning model to the extracted software log data to generate a pipeline health check analysis report, wherein the pipeline health check analysis report includes an automatically generated prediction associated with future operation of the software continuous integration and/or deployment pipeline; and transmitting, via a communication output port coupled to the intelligent software agent platform, the pipeline health check analysis report via a distributed communication network; wherein the pipeline health check analysis report includes: a prediction of a level of stability of the software continuous integration and/or deployment pipeline; a prediction of a future type of failure of the software continuous integration and/or deployment pipeline; a listing of a software object associated with an error condition; a number of test jobs associated with an application program; a number of jobs triggered that were associated with the application program; and a percentage of software builds that have successively passed through the software continuous integration and/or deployment pipeline during a time period.
19. The medium of claim 18 , wherein the pipeline health check analysis report is transmitted to an automation framework and includes at least one of a self-healing analysis and a recommended corrective action.
20. The medium of claim 18 , wherein the pipeline health check analysis report comprises an email message transmitted to at least one of: (i) a subject matter expert, (ii) a software development engineer in test, (iii) a software manager, (iv) a quality control member, (v) a quality assurance member, or (vi) any other stakeholder.
21. The medium of claim 18 , wherein the pipeline health check analysis report is used to automatically transmit a remote access Application Programming Interface (“API”) console output to the software continuous integration and/or deployment pipeline.
22. The system medium of claim 18 , wherein the pipeline health check analysis report includes information about multiple software continuous integration and/or deployment pipelines.
23. The medium of claim 18 , wherein the pipeline health check analysis report includes at least one recommended action.
This invention relates to monitoring and analyzing the health of data processing pipelines, particularly in systems where multiple data processing stages are interconnected. The problem addressed is the lack of automated tools to assess pipeline health, identify issues, and provide actionable recommendations to maintain or restore optimal performance. The system generates a pipeline health check analysis report that evaluates the operational status of a data pipeline. The report includes metrics such as data flow rates, error rates, latency, and resource utilization across different pipeline stages. Additionally, the report identifies potential issues, such as bottlenecks, failures, or inefficiencies, by comparing current performance against predefined thresholds or historical data. A key feature is the inclusion of at least one recommended action in the report. These recommendations are derived from the analysis and may suggest adjustments to pipeline configurations, resource allocation, or error handling strategies. The recommendations aim to improve pipeline reliability, efficiency, or performance. The system may also prioritize recommendations based on severity or impact, ensuring critical issues are addressed first. The invention is particularly useful in large-scale data processing environments where manual monitoring is impractical, helping to automate pipeline maintenance and reduce downtime. The recommended actions provide a proactive approach to pipeline management, allowing operators to address issues before they escalate.
24. The medium of claim 18 , wherein the intelligent software agent platform is further to integrate the pipeline health check analysis report into a dashboard display.
25. The medium of claim 18 , wherein the machine learning model is associated with at least one of: (i) artificial intelligence, (ii) supervised learning, (iii) semi-supervised learning, (iv) weakly supervised learning, (v) unsupervised learning, (vi) reinforcement learning, (vii) feature learning, (viii) sparse dictionary learning, (ix) anomaly detection, (x) decision trees, (xi) association rules, (xii) an artificial neural network, (xiii) a support vector machine, (xiv) a Bayesian network, and (xv) a genetic algorithm.
Unknown
March 30, 2021
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.